SPSS Version 14.0 05/07/2007

INTRODUCTION TO SPSS PART I............................................................................................................................................................ 2 INTRODUCTION............................................................................................................................................ 2

Background ................................................................................................................................................ 2 Starting SPSS............................................................................................................................................. 3 Inputting Data ............................................................................................................................................. 3 Defining Variables ...................................................................................................................................... 3 Variable and Value Labels ......................................................................................................................... 3 Reviewing Variables................................................................................................................................... 3 Entering Data.............................................................................................................................................. 3

FILE MANAGEMENT..................................................................................................................................... 3 Saving an SPSS for Windows XP File ....................................................................................................... 3 Backing Up Your Data................................................................................................................................ 3 Retrieving Data Files .................................................................................................................................. 3 Reading An Excel File Into SPSS .............................................................................................................. 3

INITIAL DATA CHECKING ............................................................................................................................ 3 Case Summaries........................................................................................................................................ 3

DESCRIPTIVE STATISTICS ......................................................................................................................... 3 Frequency Tables....................................................................................................................................... 3 Descriptives ................................................................................................................................................ 3 Cross-tabulation ......................................................................................................................................... 3 Three-way tables........................................................................................................................................ 3

EDITING AND MODIFYING THE DATASET ................................................................................................ 3 Inserting Data ............................................................................................................................................. 3 Deleting A Case.......................................................................................................................................... 3 Inserting A Variable .................................................................................................................................... 3 Deleting A Variable..................................................................................................................................... 3 Deleting An Entry In An Individual Cell....................................................................................................... 3 Moving A Variable ...................................................................................................................................... 3 Manoeuvring Between Windows................................................................................................................ 3 Moving around Data Editor......................................................................................................................... 3

PART II........................................................................................................................................................... 3 CONSTRUCTING NEW VARIABLES............................................................................................................ 3

Computing a New Variable......................................................................................................................... 3 Computing a New Variable by using built-in Functions.............................................................................. 3 Computing Duration of Time Difference by built-in Functions.................................................................... 3 Recoding a value........................................................................................................................................ 3 Selecting a Subset of the Data................................................................................................................... 3

GRAPHS........................................................................................................................................................ 3 Bar Charts .................................................................................................................................................. 3 Histograms ................................................................................................................................................. 3 Scatter Plots ............................................................................................................................................... 3 Plotting a Regression Line on a Scatter Plot.............................................................................................. 3

STATISTICAL INFERENCE IN SPSS ........................................................................................................... 3 Introduction................................................................................................................................................. 3 Categorical Variable ................................................................................................................................... 3 The Chi-squared test and Fisher’s Exact test ............................................................................................ 3

CONTINUOUS OUTCOME MEASURES ...................................................................................................... 3 Comparison of Means Using a t-test .......................................................................................................... 3

LINEAR REGRESSIONS............................................................................................................................... 3 Model Checking.......................................................................................................................................... 3

NON-PARAMETRIC METHODS ................................................................................................................... 3 COMPARISONS OF RELATED OR PAIRED VARIABLES .......................................................................... 3

Continuous Outcome Measures................................................................................................................. 3 Analysis of Binary Outcomes that are Related........................................................................................... 3 Related Ordinal Data.................................................................................................................................. 3

LOGISTIC REGRESSIONS........................................................................................................................... 3 Model Checking.......................................................................................................................................... 3

CREATING A SPSS SYNTAX....................................................................................................................... 3

SPSS Version 14.0 05/07/2007 2

PART I

INTRODUCTION Background This handbook designed to introduce SPSS for Windows XP. It assumes familiarity with micro-

soft windows and standard windows-based office productivity software such as word processing

and spreadsheets.

SPSS for Windows XP is a popular and comprehensive data analysis package containing a

multitude of features designed to facilitate the execution of a wide range of statistical analyses. It

was developed for the analysis of data in the social sciences - SPSS means Statistical Package for

Social Science. It is well suited to analysing data from surveys and database.

This practical uses a set of data from a cross-sectional survey of respiratory function and dust levels

amongst foundry workers. The object of the survey data is to determine whether the dust levels

found in the foundries have any effect on the respiratory function.

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Starting SPSS After logging on to Windows XP, the user will be presented with a screen containing a number of

different icons. First click on the icon ACCESS to Datafiles. This enables access to the files on

drive L: that are used in this session. Once this has completed, start SPSS by clicking the Start

button then selecting

All Programs Programs Core Statistics SPSS 14.0 SPSS14.0

Then the SPSS 14.0 for Windows XP screen will appear and it is called Untitled – SPSS Data

Editor (shown below). In the middle of the Data Editor screen you can see another window

asking the question ‘What would you like to do?’

There are five options listed with icons:

• Run the tutorial

• Type in data

• Run an existing query

• Create new query using Database Capture Wizard

• Open an existing file

If you choose the first Run the tutorial, the following screen is presented.

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If you don’t want to run tutorial, just click Cancel button then you get data editor screen.

If you choose the second option Type in data, you will get the data editor screen at which you can

input data into a new SPSS data file.

The third option Run an existing query and the fourth option Create new query using Database

Capture Wizard are not relevant to this course.

The final option Open an existing file allows one to open and existing SPSS data file selecting

from the list shown in the Window below.

Inputting Data

The SPSS Data Editor screen looks like a spreadsheet but there are some important differences.

Each row represents the data for a case. A case could be a patient or a laboratory specimen. It could

also be a set of results for a patient at a particular time. Each column represents a variable. A

variable could be the answer to a question or any other piece of information recorded on each case.

Before you enter any data in the spreadsheet you have to create a variable for the information you

have collected. You must define a variable for each question in your data set you plan to analyse.

Defining Variables If you look at the left hand corner at the bottom of the SPSS Data Editor screen, you will see two

little tabs: One says Data View and another says Variable View. To create a variable click on the

Variable View then the following screen will appear.

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Each row of the table describes the attribute of one variable. Begin by entering a variable name in

the Name column. Variable Names can be up to 64 characters long, contain no spaces and it is

important to use something meaningful. It is best to stick to alphanumeric characters and start with

a letter. Once you have entered a name, SPSS defines the variable type as Numeric. You may need

to change the variable type, for example to String if you want to character data such as names or to

Date if you want to enter dates. To do this, click on the cell corresponding to the Type column. A

little combo button will appear there. If you click that button then you will get the following

screen with 8 options of type.

You will usually be working with Numeric, Date or String type of data. For Numeric variables

you may want to change the decimal places. If the data are integers (whole numbers) such as age in

complete years you could alter the decimal places to zero. If the numbers you are planning to enter

are very small (0.00072) or you require a high level of precision (21.7865) you may want to

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increase the number of decimal places. Usually there is no need to change the width from 8, note

that width must be larger than the number of decimal places.

For date variable it is best to use a 4 digit year (dd.mm.yyyy)

With text strings you are given the option to change the number of characters

Where possible you are strongly advised to use numerical coding rather than strings. This makes

statistical analysis easier. If you are entering string data is longer than 8 characters, you will need to

increase the Width from the default of eight. To be able to fully display the string in data view

window you may need to increase the numbers of columns in the variable view window.

The column missing in the variable view window allows you to define which codes correspond to

missing values. You can have several values allowing you to distinguish between missing data due

to the respondent forgetting to answer rather than say not applicable or refused to answer. For

example, a code of 8 could indicate not applicable, and 9 that would indicate the respondent had

missed a question out. If a value is defined as a missing value code for a particular variable, subjects

with that code will be dropped from the analysis of that variable.

To set up missing value codes for a variable, click on the cell of the Missing column. Click on

Discrete missing values and enter the missing values for this variable in the boxes below (Up to 3

can be entered). To complete the entry press OK

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Variable and Value Labels There are two types of labels in SPSS. A variable label allows you to give a variable a longer and

clearer description that will be displayed on the statistical output such as graphs and tables.

The second is value label. This enables you to describe each of the values a variable may have.

These labels will be displayed on tables improving readability. For example Exposure group in the

following practical has 2 values “Unexposed” and “Exposure to dust” which have been coded as

“0” and “1”. The label option in the variable view window also allows you to define labels for

missing values.

To define a variable label click on cell of Label column of the Data Editor screen and enter your

description of the variable.

To define Value Labels - click the cell of the value column and then the click on the combo button

then enter the Value: window and its associated label against the Value Label window: then press

Add. The added label will then appear in the window below.

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Once you have entered all the value labels for a variable press OK .

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Exercise The table below lists the variables from the foundry study. Set-up the following variables

Variable Name

Description (Variable Label )

Type Width Column Extras Value Labels for each code

idno Identification No Numeric 4 5 group Exposure Group Numeric 1 6 Labels 1 = Exposed to dust

0 = Unexposed age Age at assessment Numeric 2 8 sex Numeric 1 4 Labels 0 = female

1 = male ht Height in cms

Numeric 3 5

fevmeas Measured FEV Numeric 3 with 2 decimal places

8

fevpred Predicted FEV Numeric 3 with 2 decimal places

8

fvcmeas Measured FVC Numeric 3 with 2 decimal places

8

fvcpred Predicted FVC Numeric 3 with 2 decimal places

8

asthma Ever had asthma Numeric 1 7 Labels 0 = No 1 = Yes 2 = Don’t Know

bron Ever had Bronchitis

Numeric 1 5 Labels 0 = No 1 = Yes 2 = Don’t Know

smknow Do you smoke now

Numeric 1 8 Labels 1 = Yes 0 = No

smkever Have you ever smoked

Numeric 1 8 Labels 0 = No 1 = Ex smoker 2 = Current smoker

cigno No of cigarettes per day

Numeric 3 6 Missing Value

-88

cigyrs No of years smoked

Numeric 3 8 Missing Value

-88

empyrs No of Years with company

Numeric 2 7

respdust Current exposure Numeric 3 with 2 decimal places

9

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Reviewing Variables

Once you have created all these variables, you can check they have been set up correctly. To do this

click from the menu bar Utilities then Variable and choose the variable you require and the

following screen should appear.

Entering Data

When you finish creating all the variables you click Data View and you get the following screen

with all the variable names at the top of the spreadsheet.

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You can now enter the data as you would in a spreadsheet. To make an entry in a particular cell on

the spreadsheet use the mouse to move the cursor to select that cell and type in the value. The value

will appear in the cell. Click on the mouse, press enter or use the cursor keys to enter that value.

If you attempt to enter data of the wrong type into a variable (for example text into a numeric

variable) the data will not be accepted. If incorrect data is entered, it can be overtyped or deleted.

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Exercise The data below is from the foundry study for which you have just entered the variable codes. If you leave a gap in any cell in the worksheet,

SPSS will put a dot (.) and treat it as missing data. Once you have entered all the cases, it is useful to display the Value Labels of the coded values.

These are displayed by using choosing value labels button from the second row of options at the top of either the Data view or Variable View window.

Idno group age Sex Ht fevmeas fevpred fvcmeas fvcpred asthma bron smknow smkever cigno cigyrs empyrs respdust

1001 Exp. 49 Female 175 3.40 3.59 4.49 4.45 No No Yes Curr 20 31 23 1.71

1002 Exp. 46 Female 168 2.83 3.39 3.91 4.12 Yes No Yes Curr 20 11 16 0.69

1003 Non 34 Female 180 3.93 4.26 4.80 5.14 No No No Never 12 0.00

1004 Non 34 Male 180 4.01 4.25 4.57 5.12 No No Yes Curr 25 16 12 0.00

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FILE MANAGEMENT Saving an SPSS for Windows XP File Once you have entered some data you will want to save it to disk. It is good practice to save data at

regular intervals during data entry just in case!

To save the data you have just entered, click on the File option at the top left corner of the screen

and then on the Save As... sub-option.

The following screen will appear:

To save a copy of the current SPSS for Windows XP file on your floppy disk, under Drives: click

on 7 in the save in window to generate a list of the drives.

Click on the up-arrow to move to the 31/2 Floppy (A): drive and move the cursor to the File name

window and enter a suitable name. By default SPSS will add the file extension .sav. Finally, click

on the Save button. It will help to identify the file as a SPSS datafile if the file extension .sav is

used.

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Backing Up Your Data It is good practice to save data on different disks and also several names as data entry progresses

(e.g. mydata1 mydata2 etc). To make a backup copy of your data repeat the Save Data As

procedure.

Retrieving Data Files Retrieving an SPSS for Windows XP File is essentially the reverse of this process. Click on the File

option at the top of the screen, then on the Open sub-option followed by the Data option.

The following screen will appear. Then select the required file from the window.

We can also open a data file when we as start an SPSS session (see above).

Reading An Excel File Into SPSS

Often data may be already stored in another data format. SPSS can read many of these. For example

you can retrieve an Excel file into SPSS. If you put the variable names in the first row of your

speadsheet, they can be copied as variable names in SPSS file. Unlike StatsDirect, SPSS is only

able to read a single work sheet it cannot read a complete work book with several sheets. In order

that SPSS can read it, the Excel file needs to be saved in the version 4 format.

The data from the foundry study is saved in a spreadsheet L:\spssco~1\foundry. The names of the

variables have been entered in the first row. You may wish to check this by going to EXCEL . The

procedure for retrieving the data from EXCEL is similar to retrieving an SPSS data file. Click on

the File option at the top of the screen, then on the Open sub-option followed by the Data option so

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that the screen above appears. At this point change the file type to Excel and Open the spreadsheet

named foundry . The following screen should appear.

Unless there is other data on the spreadsheet that we do not want to read we need not specify a

range. As we want to read the variable names, you have to click Read variable names button then

press OK . You will get an output window explaining variable names, types and their formats.

If you switch over to Data editor screen by clicking Window option on the menu bar or by using

the button on the status bar at the bottom of the screen, you will be able to see the variable names

and values in their proper columns. Now all the Foundry data has been read from the spreadsheet. If

we want to add variable labels and value labels we would need to go to variable view.

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If you don't have variable names in the Excel file then when retrieving it into SPSS file you should

not click Read variable names button, just press OK button and you get the following screen.

You then have to define the variable names by clicking the Variable View as described above.

Having read data from an excel spreadsheet it is important to check what has been read in.

For example if a column on the spreadsheet contained a mix of numeric and string data

(besides the variable name at the top) either one or the other may be set to missing.

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INITIAL DATA CHECKING For the next stage you need to retrieve the data file foundry which contains the data with variable

and value labels. This can be done in two ways, either click Access to data icon on the desktop then

L: drive then spssco~1 then foundary.dat. Alternatively open Internet Explorer and insert the

address www.medicine.manchester.ac.uk/ehs/statistics/statsnotes/ and click the link foundry.sav for

the dataset followed by open.

Case Summaries

With any data set it is extremely IMPORTANT that you check the data entered as carefully as

possible. One way you can do this is, to use case summaries. To run this, you use Analyze, Reports

and then Case Summaries.

The facility allows you to look at a column or columns separately from the rest of the data.

Highlight the variables you want to display and use the arrow to move them under Variables.

Click on the Limit cases to first 100 so that all cases will

be displayed. The following output appears.

It is then easy to see any potential errors e.g. if there was

"never" in ever smoked and "yes" in do you smoke now,

there has been an error made. The left-hand side column is

the case number.

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DESCRIPTIVE STATISTICS The first step in data analysis is to generate descriptive statistics. This will give us a feel for the

data. It will also help us identify any inconsistencies that there may be in the data. This is

sometimes called data cleaning. Techniques that are commonly used to do this include:

� Frequency Analyses

� Descriptive Statistics

� Cross-tabulations

� Plots

Frequency Tables A basic way to check for data errors is by carrying out a frequencies analysis on variables, to do this

click on the Analyse tile choose the Descriptive Statistics option and then choose Frequencies.

Move the variables of interest into the Variables box on the right-hand side, then click on Statistics

to select some summary statistics such as range, maximum, minimum, mean and median, which

will help you look for errors.

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The following screen will appear.

To select the variable to perform a frequency table, click on its name in the left hand list and then

press . Finally click on OK and the following output is then generated.

Exposure Group

Frequency Percent Valid Percent Cumulative

Percent Unexposed 63 46.3 46.3 46.3 Exposure to Dust 73 53.7 53.7 100.0

Valid

Total 136 100.0 100.0

To return to the data editor click on Window and take the data editor option from the list.

With the frequency table you can have a list of summary statistics as well. To do that you click on

Analyze, Descriptive Statistics, Frequencies. Bring the variable (say, ht) to the Variable(s)

window then click on Statistics option to select some summary statistics. Click on Continue and

OK button. Frequency tables can be copied into word processing documents by clicking on the

table and selecting Edit then Copy. To place in the word processing document, use Edit and Paste.

Output from Frequencies with some summary statistics

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Exercise Using the frequencies options find out

� what proportion of the foundry workers were exposed to dust?

� what proportions had ever suffered from bronchitis?

� what proportion had ever smoked?

� what proportion smoked more than 40 cigarettes per day?

Descriptives The descriptives command in SPSS is useful for summarizing quantitative data. To use this click on

the Analyse tile choose the Descriptive Statistics option and then choose descriptives. Move the

variables of interest into the Variables box on the right-hand side. As with the frequencies

command we can obtain descriptive statistics for several variables at once. In the panel below we

have chosen some of the quantitative variables in the foundry data set.

Statistics

Height in cms136

0

172.97

.567

173.00

175

6.613

43.732

.429

.208

.393

.413

34

158

192

23524

Valid

Missing

N

Mean

Std. Error of Mean

Median

Mode

Std. Deviation

Variance

Skewness

Std. Error of Skewness

Kurtosis

Std. Error of Kurtosis

Range

Minimum

Maximum

Sum

Height in cms

1 .7 .7 .7

3 2.2 2.2 2.9

1 .7 .7 3.7

6 4.4 4.4 8.1

7 5.1 5.1 13.2

1 .7 .7 14.0

5 3.7 3.7 17.6

14 10.3 10.3 27.9

19 14.0 14.0 41.9

1 .7 .7 42.6

8 5.9 5.9 48.5

7 5.1 5.1 53.7

1 .7 .7 54.4

26 19.1 19.1 73.5

7 5.1 5.1 78.7

5 3.7 3.7 82.4

12 8.8 8.8 91.2

2 1.5 1.5 92.6

2 1.5 1.5 94.1

3 2.2 2.2 96.3

4 2.9 2.9 99.3

1 .7 .7 100.0

136 100.0 100.0

158

160

162

163

165

166

167

168

170

171

172

173

174

175

177

178

180

182

183

185

190

192

Total

ValidFrequency Percent Valid Percent

CumulativePercent

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Exercise Use the descriptive procedure to determine

� the current mean exposure to dust per day

� the mean number of cigarettes smoked per day

For mean number of cigarettes per day you may get a negative answer. Check the missing value

codes and redo.

Cross-tabulation To examine the relationship between two categorical variables, a two way Frequency Table can be

used. This is called a cross-tabulation. Click on Analyze then Descriptive Statistics and then

Crosstabs. The screen below appears. Suppose we wished to examine how smoking status related

to exposure. We could examine this by a cross-tabulation of the variables group and smkever.

Select the smoking status variable smkever labelled Have you ever smoked in the source list then

click by the Row(s) box to make this the row variable

Select group labelled Exposure Group in the source list and click by the Column's box to

select the column variable. Finally press OK

The following result appears when the two frequency table has been completed.

Have you ever smoked * Exposure Group Crosstabulati on

Count

24 20 44

19 19 38

20 34 54

63 73 136

Never

Ex Smoker

Curr. Smoker

Have youever smoked

Total

UnexposedExposureto Dust

Exposure Group

Total

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Two way frequency tables are more informative if they include percentages. To add percentages to

the table select Cells from the Crosstabs screen. On pressing Cells, the following screen appears.

Column, row, or total percentages can be selected by clicking the appropriate box. Whilst it is

tempting to click all three this can make the output confusing. For the table above column

percentages are the most useful as they will allow us to compare the smoking status of non-exposed

and exposed subjects. By clicking column we get the resulting table.

Have you ever smoked * Exposure Group Crosstabulati on

24 20 44

38.1% 27.4% 32.4%

19 19 38

30.2% 26.0% 27.9%

20 34 54

31.7% 46.6% 39.7%

63 73 136

100.0% 100.0% 100.0%

Count

% within Exposure Group

Count

% within Exposure Group

Count

% within Exposure Group

Count

% within Exposure Group

Never

Ex Smoker

Curr. Smoker

Have youever smoked

Total

UnexposedExposureto Dust

Exposure Group

Total

Three-way tables

You may need to do comparisons on three variables. To do this, choose Analyze then Descriptive

Statistics and then Crosstabs. Then the following screen appears. To create a three dimensional

table instead of a two dimensional table, click on a variable and move using to layer 1 of 1 box.

If we add the variable sex we will now get separate tables for men and women giving the following

output.

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Do you smoke now * Exposure Group * SEX Crosstabula tion

21 13 34

63.6% 43.3% 54.0%

12 17 29

36.4% 56.7% 46.0%

33 30 63

100.0% 100.0% 100.0%

22 26 48

73.3% 60.5% 65.8%

8 17 25

26.7% 39.5% 34.2%

30 43 73

100.0% 100.0% 100.0%

Count

% within Exposure Group

Count

% within Exposure Group

Count

% within Exposure Group

Count

% within Exposure Group

Count

% within Exposure Group

Count

% within Exposure Group

No

Yes

Do you smokenow

Total

No

Yes

Do you smokenow

Total

SEXmale

female

UnexposedExposureto Dust

Exposure Group

Total

EDITING AND MODIFYING THE DATASET

Having done some preliminary analysis we may need to change the data. There are some useful

functions for modifying datafiles.

Inserting Data You may have noticed that idno 1008 was missing.

To insert it,

Click on use Data then Insert Case (immediately before IDNO 1009) and a new blank row is

added shown below.

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You can insert the following case (idno 1008) in the blank line

Variable Value Variable Value

Idno 1008 Asthma 0 Group 1 Bron 0 Sex 1 Smknow 1 Ht 180 Smkever 2

Fevmeas 4.01 Cigno 30 Fevpred 4.45 Cigsyrs 20 Fvcmeas 4.90 Empyrs 10 Fvcpred 5.30 Respdust 2.04

Deleting A Case

To delete a case, click on its number on the left of the Data Editor to highlight the row containing

the case. Press the Delete button (alternatively, click on the Edit option on the menu bar then click

on the Cut option) and the case is deleted and the cases below move up to fill the gap.

Exercise Delete case no 1008

Inserting A Variable

To insert a variable into the middle of the data, click on the variable after the position at which you

wish the variable to appear and then click on Data then Insert Variable. A blank column is

inserted before the selected variable shown here.

Exercise Insert any variable say wt.

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Deleting A Variable

To delete a variable, click on its name at the top of the Data Editor to highlight the column

containing the variable. Then press the Delete button. The variable is deleted and the variables to

the right move to the left to fill the gap. Now delete the variable you just created.

Deleting An Entry In An Individual Cell

To delete an entry in an individual cell, click on the Edit option on the menu bar and click on the

cut or clear option. Alternatively, press the Delete button

Moving A Variable

Insert a blank variable as mentioned above in the required position. Click on the name of the

variable to be moved (This highlights the column), Edit and Cut. Click on the name of the blank

variable and Edit then Paste.

Manoeuvring Between Windows To manoeuvre between data editor and output screen, click on the Window option at menu bar and

from the drop down menu click on the required option (the active screen is ticked on).

Alternatively choose the window from the status bar at the bottom of the screen.

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Moving around Data Editor You may wish to try the following commands that will assist you in moving around the Data Editor. Key Description Home First Variable End Last Variable Crtl and (Up Arrow) First Case Ctrl and (Down Arrow) Last Case Ctrl and Home First Value Ctrl and End Last Value Variable tool (Question Mark and a vertical line of cells)

Allows you to select the variable to go to

Goto Case (arrow and the horizontal line of cells)

Allows you to go to a specified case

Find tool (Field Glasses) Allows you to look for a specified value in a variable

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PART II

CONSTRUCTING NEW VARIABLES Sometimes we need to compute new variables from the data entered. For example in the foundry

data set we might want to compute the ratio of the measured to predicted fev. Alternatively we

might want to group ages into bands. SPSS has procedures to construct a new variable from

existing variables.

Computing a New Variable For the foundry worker data we shall compute the variable fevratio defined as fevmeas/fevpred.

Click Transform then Compute and the following screen appears:-

Enter the name fevratio in Target variable window. If the variable is new, click on Type & Label

to define the type and variable label. To build up mathematical expression which will create the

new variable you can choose variables from the left hand box then click to move them to the

numeric expression window. You can choose any of the keys on the calculator pad in the centre or

any of the functions from the built-in functions box followed by.

Select the function using up and down arrow key from the Built in function window and

then click on the button .The expression will appear in the Numeric Expression window

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These are the functions on the calculator pad are defined as follows.

Operator Mnemonic form

Description Operator Mnemonic form Description

+ Addition >= GE Greater Than Or Equal To

- Subtraction = EQ Equals * Multiplication ~= NE Not Equals / Division & AND Logical And ** Power Of | OR Logical Or < LT Less Than ( ) Parentheses > GT Greater Than ~ NOT Logical Not <= LE Less Than Or Equal

To

To compute fevratio we move fevmeas and fevpred into the numeric expression window. You

can also type a formulae into the numeric expression window. This is illustrated below.

Once the expression is complete press OK.

Computing a New Variable by using built-in Function s In the Compute procedure there is a built in functions window which can be used to create a new

variable or to transform the values of an existing variable. Transformations such as the square root,

or the logarithm, are easily made. Suppose you wish to do a log transformation of the variable

called height (ht) from the foundry data set. First click Transform from menu bar and then choose

Compute from drop down menu, then you get the compute window.

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Type a name, say lht , in the target variable window. Click on the arrow on the right of the

Functions box to scroll up and down through the functions. Select Arithmetic followed by Ln

function in the Functions and Special Variables box for natural log and click on Functions : 5 ,

this will put the function with a ? in parentheses in the window named Numeric Expression. Then

select the variable to replace ? i.e. ht by clicking 4 and then press OK button. Then a new

variable lht will be created (located at the end of the variable list). Having carried out a

transformation it is important to check the result. For example, taking a log of a negative value

creates a missing value. Other commonly used transformation functions are LG10, SQRT, ABS,

TRUNC etc.

Computing Duration of Time Difference by built-in F unctions In the same data set there are some variables (date of birth, date of assessment etc) which are stored

in date format. One is able to calculate the time difference (in days) by using the functions

Ctime.Days

The age of the patients on the date of assessment can be calculated from the date of birth and

assessment date. As before click Transform from menu bar and then Compute from drop down

menu, you then get the compute window. In the target variable window type a name say howold,

then select the functions group Time Duration Extraction followed by Ctime.Days in the

Functions and Special Variables window using the up and down arrow keys, click on Functions :

5 , this will put the function with a ? in parentheses in the box named Numeric Expression. Then

select the variable to replace ? i.e. date of assessment by clicking 4 . Perform the same procedure

for date of birth. You can then compute the difference Time (in days), then you have to divide the

SPSS Version 14.0 05/07/2007 30

whole thing by 365 (number of days in quarterly leap year) to get howold in years. Below is the

example.

Whenever you compute a new variable from existing data it is important to check that what you

have created is sensible. You also need to check that missing values have not been converted into

none missing values. Using the Data view tab check the value of howold.

Exercise Calculate the duration of the patients in the employment and compare with the values of

employment years provided in the data set.

Recoding a value To assist in data analyses you often need to group a continuous variable (e.g. age) into categories

To do this select Transform then Recode. Two options are now given

� Into Same Variables

� Into Different Variables

The first option leads to potentially valuable information being overwritten. It is usually best

to use the second option as it is then possible to check whether the recode has worked

correctly by comparing the new and old version.

Having chosen the second option the following screen will appear. First choose an input variable

from the list on the left hand side then press .

SPSS Version 14.0 05/07/2007 31

Then enter the name of the variable for the recoded data under Output Variable Name and press

Change.

Now press Old and New Values and the following screen appears. Suppose we wish to recode age into bands <30, 30-39, 40-49, 50+

Click on Range Lowest Through and enter 29 into the box then click on value under New value

and enter 1 and finally press Add.

Click on Range then enter 30 and 39. Then click on New Value and enter 2 and finally press Add.

Click on Range then enter 40 and 49. Then click on New Value and enter 3 and finally press Add.

Finally click on Range Through highest enter 50 then click on New Value and enter 4 and finally

press Add.

Once you have specified all the OLD -> New recodes, click on Continue then OK on the Recode

into Different Variables screen. The following shows an example of setting up a recoded value.

SPSS Version 14.0 05/07/2007 32

After recoding a variable it is usually advisable to run case summaries to compare the old and new

values

Selecting a Subset of the Data In addition to analysing the full set of data, you may want to analyse a subset. If, for example, you

want to perform an analysis on exposed cases only, click on the Data option at the top of the Data

View screen, then on the Select Cases option and the following screen will appear:

To make the selection, click in the circle with the If Condition is Satisfied box, then click the If...

button. The following screen will then appear:

SPSS Version 14.0 05/07/2007 33

To make the selection, click in the circle with the If Condition is Satisfied box, then click the If...

button. The following panel will then appear. (group = 1 has been entered in the box provided to

select the exposed cases),

Click on the Continue tile at the bottom of the screen. Once you have returned to the main Select

Cases screen, click on the OK button. The effect of the above filter on the data is shown below.

Please note the / on the left hand side showing the records which have been excluded. To remove

the filter click on Data then Select Cases and Select all cases.

Note In order to return to the complete data set for further analyses you need to return to the select

cases option and click the all cases button.

SPSS Version 14.0 05/07/2007 34

GRAPHS SPSS will produce good quality high- resolution statistical graphics. We will look at Bar Charts,

Histograms, and Scatter Plots with regression lines.

Bar Charts Bar Charts can only be produced for categorical variables e.g. Ever smoked Asthma etc

To produce a Bar Chart click Graphs, then Bar and the following screen appears.

Click on Simple and then Define and the next screen will appear. You then move your chosen

variable from the left hand list to the Categorical Axis and press OK.

Curr. SmokerEx SmokerNever

Have you ever smoked

60

50

40

30

20

10

0

Cou

nt

SPSS Version 14.0 05/07/2007 35

Histograms Histograms are produced for interval variables e.g. age. To produce a histogram click on Graphs

then Histogram and the following screen appears.

Click on the required variable, in this case FEV, in the left hand side list and press then press

OK. If you require a normal curve to be drawn on the graph click on Display normal curve.

This is the Histogram produced for measured FEV.

6.005.004.003.002.001.00

Measured FEV

25

20

15

10

5

0

Fre

quen

cy

Mean = 3.7938Std. Dev. = 0.73936N = 136

SPSS Version 14.0 05/07/2007 36

Scatter Plots Scatter plots show the joint behaviour of two interval variables. If you want to decide whether two

interval variables are related in any way you should first draw a scatter plot.

Scatter plots have 2 axes:

� the value of the dependent or response variable on the y axis.

� the value of the independent variable on the horizontal axis.

To run a scatter plot click Graphs Scatter

and the following will appear.

Click on Simple and then select variables

The above selection produces the following graph

50403020100

No of years smoked

1.40

1.20

1.00

0.80

0.60

0.40

fevr

atio

Exposure to Dust

UnexposedExposure Group

SPSS Version 14.0 05/07/2007 37

Plotting a Regression Line on a Scatter Plot

To fit a line of regression, double click on the graph. This moves the graph into the Chart Editor.

A Regression line can be added by clicking on Elements then Fit Line Total if you have not

defined any markers or Fit Line Subgroups if you have defined markers.

This produces the following graph.

50403020100

No of years smoked

1.40

1.20

1.00

0.80

0.60

0.40

fevr

atio

Fit line for Exposure toDust

Fit line for Unexposed

Exposure to Dust

UnexposedExposure Group

R Sq Linear = 0.032

R Sq Linear = 0.111

SPSS Version 14.0 05/07/2007 38

STATISTICAL INFERENCE IN SPSS Introduction This part will introduce the basic methods of statistical inference available in SPSS. It will assume

some familiarity with concepts in statistical inference including hypothesis testing and confidence

intervals. If you are unfamiliar with these concepts, you are strongly recommended to read an

introductory text in medical statistics such as Campbell and Machin “Medical Statistics A Common

Sense Approach”. Some example are given at the Medical Statistics support web site at

http://www.teaching-biomed.man.ac.uk/resources/informatics/statistics/

The methods will be illustrated by the Foundry data set that was considered in Part I. The purpose

of this study was to examine whether dust increased respiratory morbidity. In this study the measure

of respiratory morbidity are “Ever had asthma", “Ever had bronchitis”, “Measured FEV” and

“Measured FVC”. The variable “Predicted FEV” and “Predicted FVC” are the values that are

expected for a person’s demographic characteristics including Age, Height and Sex. Exposure to

dust is measured by two variables “Exposed/Un-exposed” and dust levels recorded only for exposed

workers. Because smoking is a confounding factor in this study, smoking behaviour has been

recorded in terms of current smoking status (smknow), smoking history (smkever), and

consumption (cigno) and duration of smoking (cigyrs).

During this part of the practical you may need to refer to the notes from Part I. If you are starting

the tutorial at this point rather than continuing from Part I, you will need to open the dataset

L:\SPSS\Course\foundry.sav.

Categorical Variable In the first part of the study we examined whether there was any relationship between exposure to

dust and smoking. Using the cross-tabs procedure we can generate the following table.

Do you smoke now * Exposure Group Crosstabulation

43 39 82

68.3% 53.4% 60.3%

20 34 54

31.7% 46.6% 39.7%

63 73 136

100.0% 100.0% 100.0%

Count

% within Exposure Group

Count

% within Exposure Group

Count

% within Exposure Group

No

Yes

Do you smokenow

Total

UnexposedExposureto Dust

Exposure Group

Total

SPSS Version 14.0 05/07/2007 39

From the table above it can be seen that the percentage of workers who currently smoke is higher

for those exposed to dust than those who are not, 47% as compared to 32%.

We will now examine whether respiratory symptoms as measured by the variable asthma relate to

smoking. Using cross-tabs procedure again we obtain the following table.

Ever had Asthma * Do you smoke now Crosstabulation

77 48 125

93.9% 88.9% 91.9%

5 6 11

6.1% 11.1% 8.1%

82 54 136

100.0% 100.0% 100.0%

Count

% within Do yousmoke now

Count

% within Do yousmoke now

Count

% within Do yousmoke now

No

Yes

Ever hadAsthma

Total

No Yes

Do you smoke now

Total

The Chi-squared test and Fisher’s Exact test

Amongst those who currently smoked 11.1% had experienced symptoms of asthma whilst only

6.3% amongst those who did not. Does this suggest that smoking may be related to asthma or might

this difference be due to chance - that is explained by sampling variation? One way in which we

can examine this is by a chi-squared test. This can be carried out by re-running the cross-tab

procedure including the chi-squared statistics option as follows. In the cross-tabs panel (see

illustration below) we select Statistics to reveal the second panel that lists possible statistics. In this

panel we have selected chi-squared.

SPSS Version 14.0 05/07/2007 40

Then click on continue then OK to get the analysis below

Chi-Square Tests

1.101b 1 .294

.530 1 .467

1.075 1 .300

.344 .231

1.093 1 .296

136

Pearson Chi-Square

Continuity Correctiona

Likelihood Ratio

Fisher's Exact Test

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)Exact Sig.(2-sided)

Exact Sig.(1-sided)

Computed only for a 2x2 tablea.

1 cells (25.0%) have expected count less than 5. The minimum expected count is 4.37.

b.

The panel above gives the results of a chi-squared test of no association between asthma and

smoking. In interpreting this table one is concerned with three columns headed “Asymp.Sig” and

“Exact Sig.”. These columns give the p-values for the significance test. Firstly it is usually

recommended that you consider a 2-sided rather than 1-sided test. As one of the cells has an

expected count less than or equal to 5, it is recommended that we take the Fisher’s Exact Test value

as our result – that is 0.344. Assuming the conventional 0.05 significance level, this result is

considered non-significant. In reporting results of statistical tests you are strongly recommended to

give the p-value rather than just write “significant” or “non-significant”. In reporting this we might

write “there was no evidence of an association between smoking and asthma (Fisher’s Exact

p=0.344)." Had the expected count been greater than 5 and the table greater than 2 by 2 it is

suggested that you report the straight forward Chi-squared test p-value. If the expected count is

greater than 5 but the table is a 2 by 2 then report the continuity correction p-value.

Exercise Using the cross-tabs procedure examine whether there is a relationship between current

smoking status and bronchitis symptoms.

Are the expected numbers greater than 5 for all cells?

Fill in the spaces and delete as appropriate in the following statement:

“Amongst those that currently smoked ___% had experienced symptoms of bronchitis whereas

___% of non-smokers experience such symptoms. This was statistically significant/non significant

at a 5% level using a two-tailed continuity corrected chi-squared test with p=______ “

Exercise Now use the cross-tabs procedure to examine the relationship between Exposure to dust

and symptoms of bronchitis and asthma. Record your conclusions below using either the continuity

corrected chi-squared or Fisher’s exact test as appropriate.

SPSS Version 14.0 05/07/2007 41

We have found no statistically significant relationship between exposure to dust and either asthma

or bronchitis symptoms. For bronchitis symptoms you should have obtained the following tables.

Ever had Bronchitis * Exposure Group Crosstabulatio n

59 62 121

93.7% 84.9% 89.0%

4 11 15

6.3% 15.1% 11.0%

63 73 136

100.0% 100.0% 100.0%

Count

% within Exposure Group

Count

% within Exposure Group

Count

% within Exposure Group

No

Yes

Ever had Bronchitis

Total

UnexposedExposureto Dust

Exposure Group

Total

Chi-Square Tests

2.620b 1 .106

1.807 1 .179

2.735 1 .098

.169 .088

2.601 1 .107

136

Pearson Chi-Square

Continuity Correctiona

Likelihood Ratio

Fisher's Exact Test

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)Exact Sig.(2-sided)

Exact Sig.(1-sided)

Computed only for a 2x2 tablea.

0 cells (.0%) have expected count less than 5. The minimum expected count is6.95.

b.

Whilst 15% (11/73) of the exposed worker had symptoms of bronchitis and only 6% (4/63) of non-

exposed, this difference was not statistically significant at the 5% level (p=0.179). There are several

explanations for this. There may be no relationship between the exposure to dust and respiratory

disease. Alternatively, the study may have lacked statistical power to detect small differences. It

should be noted also that only 11% (15/136) of the sample reported such symptoms.

CONTINUOUS OUTCOME MEASURES

We will now consider the lung function measurements. Given that lung function is age and size

dependent it is usual to divide measured lung function by the expected lung function. In Part I we

constructed such a variable.

Exercise Using the Compute option in Transform construct new variable fevratio and fvcratio

defined by fevmeas/fevpred and fvcmeas/fvcpred.

We now want to examine whether workers exposed to dust have

reduced lung function. First we might examine this graphically with

a box plot. Going to the graph menu, select boxplot.

SPSS Version 14.0 05/07/2007 42

Select simple to get and transfer variable names in the usual way (see below).

This gives the following plot

Exposure to DustUnexposed

Exposure Group

1.40

1.20

1.00

0.80

0.60

0.40

fevr

atio

65

54

80

The box represents the inter-quartile range; the whiskers represent the range. The solid line in the

middle represents the median. This suggests that there is little difference between the dust exposed

and non-exposed workers. Other Analysis options we might use to compare the lung function of

exposed and non-exposed workers are Explore in the Descriptive section and the Means under

Compare Means.

Exercise Use Explore and Means options to compare lung function of exposed with non-exposed

workers using fvcratio and fevratio. Record the results below.

Mean Standard

Deviation

Median Max Min N

Exposed

Non Exposed

SPSS Version 14.0 05/07/2007 43

Comparison of Means Using a t-test

The t-test procedure can be used for statistical comparison of the mean FEV ratio of the exposed

compared to non-exposed workers. It will also give the confidence interval for the difference of the

two means. For the test go to Compare means then Independent Sample t-test

The following panel (below left) then appears into which we have selected fevrat as the test

variable and group defining the exposure.

Note (? ?) marks beside the variable name group. Click on Define Groups to add the codes for the

codes “0” and “1” for the two groups as shown (in the panel on the right).

SPSS Version 14.0 05/07/2007 44

The ability to select groups by choice of codes simplifies things when there are more than two

groups in the data set.

Clicking Continue then Ok gives the results below. The first summarises the data of the two

groups. The second presents two analyses. The first two columns of data, the Levene’s F-Test of

equality of variance – the assumption of a t-test is that the means for each group have the same

variance. The remainder summarise a t-test for equal and un-equal variance. For this data there is

no evidence that the variance as p=0.734 for the Levene’s test. Therefore we take the first row as

the t-test results although in this case it makes little difference. The result can be summarised as

“there was no evidence of increased FEV ratio for workers exposed to dust

(mean diff=0.0155, 95% c.i -0.032 to 0.063 p=0.519)”

Group Statistics

63 1.0158 .12785 .01611

73 1.0003 .14789 .01731

Exposure GroupUnexposed

Exposure to Dust

FEVRATN Mean Std. Deviation

Std. ErrorMean

Independent Samples Test

.116 .734 .647 134 .519 .0155 .02390 -.03181 .06272

.654 133.999 .514 .0155 .02364 -.03131 .06222

Equal variancesassumed

Equal variancesnot assumed

FEVRATF Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

Exercise Compare mean FVC ratio for the exposed and non-exposed subjects using a t-test

From the analyses there appears to be no evidence that exposure to dust affects respiratory function.

It may be argued nevertheless that being categorised as "exposed" or "not exposed" is a crude

assessment for exposure. Dust exposure has been recorded for subjects in the exposed group. We

will now carry out some analysis on just the exposed subjects. First we select these from the data.

This was shown in P art I of the tutorial. Under Data we choose Select cases then If condition is

satisfied as shown below. We add the condition group=1 subsequent analysis will only be on the

dust exposed group.

SPSS Version 14.0 05/07/2007 45

Below displays a scatter plot of FEV ratio compared to dust for subjects for the exposed group.

5.004.003.002.001.000.00

Current Exposure

1.40

1.20

1.00

0.80

0.60

0.40

fevr

atio

Exposure to DustExposure Group

There is some suggestion from this that respiratory function may be reduced for those with higher

exposure.

SPSS Version 14.0 05/07/2007 46

LINEAR REGRESSIONS

To test this we will use linear regression to fit a straight line of the form Y=A + BX.

Where Y is the dependent variable fevratio and X is independent variable respdust. If the gradient

B is negative, this would indicate reduced respiratory function with increased dust. To do this in

SPSS, whilst keep select cases to exposure group 1 go to the Regression then Linear as shown

In the following panel transfer the variables as shown.

SPSS Version 14.0 05/07/2007 47

There are several tables of results generated by the linear regression option. The most useful of

these is the table of coefficients shown below.

The coefficients are the values of A and B in the equation of the line fevratio=A+B.respdust

Coefficients(a)

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig.

(Constant) 1.069 .041 26.019 .000 1

Current exposure to dust -.057 .031 -.212 -1.830 .071

a Dependent Variable: fevratio The coefficient for respiratory is written -0.057. The column labelled “Sig.” gives the p-value for

the statistical test that the regression coefficients differ from zero. This tell us that the constant is

significantly different from zero which is not particularly interesting as we do not expect the

intercept of the line with the y-axis to be zero. It gives a p-value of 0.071for the test that the

gradient differs from zero. There is some suggestion of a negative gradient, but this is not

significant at the conventional 5% significance level.

The Model Summary table reproduced below tells one how well the line fits that data. The result

for R2 (written “R square”) is 0.045. This is an estimate of the proportion of the variance explained

by the model. A line that fits the data perfectly will have an R2 equal to 1. Where as a line that does

not explain anything in the data will have an R2 of zero. A value of R2 equal to 0.045 is therefore

not at all good – only 4.5% of the variation in the data is being explained.

Model Summary

.212a .045 .032 .14553Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), Respiratory Dusta.

The conclusion that can be drawn from this is that whilst there is a slight suggestion of reduced

respiratory function with increased dust exposure, the evidence is weak.

Model Checking The linear regression model described by the coefficients allows one to estimate a predicted value.

The difference between the observer value and the predicted value is called a residual. Where a

model fits badly the regression line will have large residuals. If we consider the scatter plot above

for FEV ratio compared to respiratory dust the residuals will be large. One of the assumptions of a

SPSS Version 14.0 05/07/2007 48

regression model is that the residuals will have a normal distribution. One way to check this

graphically is to use normal probability plot . This compares the residuals against a normal

distribution. Such a plot can be obtained from linear regression in SPSS as shown

Just select the normal probability plot options. Then the plot will be added to the output when it is

re-run. If the residuals are normally distributed the plotted points are on the diagonal line. The plot

below suggests that the data are approximately normally distributed. If the data were skewed the

points would bulge away from the line.

1.00.80.60.40.20.0

Observed Cum Prob

1.0

0.8

0.6

0.4

0.2

0.0

Exp

ecte

d C

um P

rob

Dependent Variable: fevratio

Normal P-P Plot of Regression Standardized Residual

Exercise Examine the relationship between FVC ratio and dust levels using the methods above.

SPSS Version 14.0 05/07/2007 49

NON-PARAMETRIC METHODS

Where data is not normally distributed, statistical analyses that assume a normal distribution may be

inappropriate. This is especially a concern where the sample size is small (<50 in total). Variables

that are discrete (take only integer values) or have an upper or lower limit are by definition non-

normal. Sometimes the distribution of the data is approximately normal so this is not a problem,

particularly where the sample size is large, but for some variables it may be unreasonable to treat

the data as normally distributed. To illustrate this we will compare the number of cigarettes smoked

by "exposed" and "non-exposed" workers who currently smoke.

Before you start this you will need to reselect all cases as follows. To do this go to Data then Select

case and change the if condition to smknow=1 as shown.

The frequency table for cigs per day for current smokers is given below.

No of cigarettes per day

2 3.7 3.7 3.7

1 1.9 1.9 5.6

1 1.9 1.9 7.4

3 5.6 5.6 13.0

2 3.7 3.7 16.7

6 11.1 11.1 27.8

1 1.9 1.9 29.6

23 42.6 42.6 72.2

6 11.1 11.1 83.3

7 13.0 13.0 96.3

2 3.7 3.7 100.0

54 100.0 100.0

3

5

6

10

12

15

18

20

25

30

40

Total

ValidFrequency Percent Valid Percent

CumulativePercent

More than half the sample (30/54) give values of 20 or 30 cigs. per day. The variable is not even

approximately normally distributed.

SPSS Version 14.0 05/07/2007 50

Exercise Use the Explore option under Descriptive statistics to determine the median and inter-

quartile range for No Cigs consumed for Exposed and Non-dust exposed workers.

Suppose we wanted to compare the median number of cigarettes smoked per day by smokers

according to dust exposure group. The method one uses is the Mann-Whitney U-test, which is

called a rank based non-parametric method. The analysis is based not on the raw data values but

on the ranks of the data. The procedure ranks the values of numbers of cigarettes smoked from

smallest to largest.

The Mann-Whitney U-Test is carried out as follows. Under Analysis select Non-parametric to

give a choice of non-parametric procedure. As we are going to compare two groups the choice in

this case is then 2-Independent Groups. In this panel select, Mann-Whitney U-test, No cigs as the

test variable and Group as the grouping variable as shown.

This generates the following output

Ranks

20 25.45 509.00

34 28.71 976.00

54

Exposure GroupUnexposed

Exposure to Dust

Total

No of cigarettes per dayN Mean Rank Sum of Ranks

Test Statistics a

299.000

509.000

-.767

.443

Mann-Whitney U

Wilcoxon W

Z

Asymp. Sig. (2-tailed)

No ofcigarettesper day

Grouping Variable: Exposure Groupa.

In the tables above note the mean rank for each group and the significance level. The mean rank is

slightly lower for the unexposed group but this is not statistically significant at a 5% significance

SPSS Version 14.0 05/07/2007 51

level. Hence, we conclude that there is no difference between the median number of cigarettes

smoked by "exposed" and "non-exposed" workers. Before moving on to the next analysis we need

to select all subjects from the data menu.

COMPARISONS OF RELATED OR PAIRED VARIABLES

For most of the analysis above we have compare the "exposed" and "non-exposed" groups of

workers. In some circumstances we want to compare measures within the same subject. Such

comparisons are sometimes referred to as paired or pair-matched.

Continuous Outcome Measures One might want to compare the mean of a continuous measure at one time point with the mean of

the same measure at a different time point. Whilst this may not be a sensible analysis for this data,

we can illustrate this for a continuous variable by comparing FEV measured with FVC measured.

To compare the mean measured FEV with mean predicted FEV we select a Paired samples T-test

in the Compare means submenu. This gives the panel below. Pairs of variables are selected by

highlighting the pair of variables in the window to the left then clicking on the select button to

transfer to the Paired Variable window as shown.

SPSS Version 14.0 05/07/2007 52

Results are given below

Paired Samples Statistics

3.7938 136 .73936 .06340

3.7552 136 .45619 .03912

Measured FEV

Predicted FEV

Pair1

Mean N Std. DeviationStd. Error

Mean

Paired Samples Correlations

136 .739 .000Measured FEV &Predicted FEV

Pair1

N Correlation Sig.

Paired Samples Test

.03860 .50632 .04342 -.04726 .12447 .889 135 .376Measured FEV -Predicted FEV

Pair1

Mean Std. DeviationStd. Error

Mean Lower Upper

95% ConfidenceInterval of the

Difference

Paired Differences

t df Sig. (2-tailed)

It is readily apparent that mean measured FEC is slightly greater than mean predicted FEV.

However, we report this as “Measured FVC was not significantly higher than measured FEV as

(meandiff=0.038, 95% c.i. -0.0473 to 0.1245, p>)”

Exercise Compare the mean measured FVC with the mean predicted FVC.

The above method of analysis compares the mean value for the two variables. It does not tell one

how close individual values are for the same subject. A visual way in which one can do this is with

a scatter plot of the two variables as shown below. We get a visual impression that FEV and FVC

are quite strongly correlated. By choosing the same numerical range for both axes we can see also

that the values for FVC are systematically larger than for FEV.

8.007.006.005.004.003.002.001.00

Measured FEV

8.00

7.00

6.00

5.00

4.00

3.00

2.00

1.00

Mea

sure

d F

VC

SPSS Version 14.0 05/07/2007 53

Analysis of Binary Outcomes that are Related

Suppose we wish to compare the proportion of workers who had bronchitis symptoms with the

proportion who had asthma symptoms. One might first construct the cross-tabulation using the cross

tabs procedure. Both row and column percentages have been added.

Ever had Bronchitis * Ever had Asthma Crosstabulati on

113 8 121

93.4% 6.6% 100.0%

90.4% 72.7% 89.0%

12 3 15

80.0% 20.0% 100.0%

9.6% 27.3% 11.0%

125 11 136

91.9% 8.1% 100.0%

100.0% 100.0% 100.0%

Count

% within Ever hadBronchitis

% within Ever had Asthma

Count

% within Ever hadBronchitis

% within Ever had Asthma

Count

% within Ever hadBronchitis

% within Ever had Asthma

No

Yes

Ever had Bronchitis

Total

No Yes

Ever had Asthma

Total

Careful examination of this table reveals that 11% (15/136) of workers reported bronchitis whilst

only 8% (11/136) had asthma. These two proportions can be compared using McNemar’s test. This

is available under 2 Related samples in the Non-parametric sub menu. Select the pair of variables

in the same way as for a paired t-test and select the McNemar option.

This gives the following results

Test Statistics(b) Ever had

Asthma & Ever had Bronchitis

N 136 Exact Sig. (2-tailed) .503(a)

a Binomial distribution used. b McNemar Test

The p-value for the McNemar test is not significant (p=0.503) so we conclude that symptoms of

bronchitis are no more common in this population than symptoms of asthma.

SPSS Version 14.0 05/07/2007 54

Related Ordinal Data For ordered categorical or quantitative variables that are not plausibly normal the suggested

procedure is to use the Wilcoxon procedure. This is selected from the same panel as McNemar Test

(see above).

LOGISTIC REGRESSIONS

It is possible to apply regression techniques to a binary outcome e.g. Ever had Asthma Yes or No

and test the effect of predictors on this outcome. We use logistic regression to fit a straight line of

the form Y=A + BX.

Where Y is a link function called logit that converts the dependent variable Ever had Asthma from

a binary (0=No 1=Yes) variable into a probability of success (i.e. probability of Yes anwer) and X

is the standard independent variable respdust. Unlike before, the gradient B is a coefficient and can

not be interpreted as linear regression, alternatively the coefficient can be altered using the

exponential function so as to be considered an odds ratio (we will explain how to interpret this later)

To do this in SPSS, go to the Regression then Binary Logistic as shown

In the following panel transfer the variables as shown.

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If the variable you wish to assess is a categorical variable then click Categorical and transfer the

appropriate variable into the appropriate box, note also click the radio button indicating Reference

Category as the first. Then finally click ok.

There are several tables of results generated by the linear regression option. The most useful of

these is the table of coefficients shown below.

The coefficients are the values of A and B in the equation of the line logit(asthma)=A+B.respdust

Variables in the Equation

B S.E. Wald df Sig. Exp(B) respdust .892 .414 4.649 1 .031 2.439 Step

1(a) Constant -3.190 .543 34.468 1 .000 .041

a Variable(s) entered on step 1: respdust.

The coefficient for respiratory is written 0.892 but the odds ratio is given as 2.439. An odds ratio

falls into three distinct groups, equal to 1, less than and greater than 1. An odds ratio of 1 indicates

no change in the likelihood of having the event (asthma) as the predictor changes (respdust). Odds

ratios greater than 1 indicate an increased likelihood of asthma and an odds ratio less than 1

indicates a decreased likelihood of asthma. So in this case we say that as respdust increase by one

unit the likelihood of having asthma increases by a multiple of 2.439. If the variable being tested

was a categorical variable say gender then one of the categories would be classed as the reference

category and the odds ratio would refer to the difference between the two groups. Say the odds ratio

was 3.2 and Males are the reference category we would say, Females are approximately 3 times as

likely as males to develop Asthma.

The column labelled “Sig.” gives the p-value for the statistical test that the odds ratios significantly

differ from one. This tells us that with a p-value of 0.031 for the test that the odds ratio for respdust

differs from one. There is strong suggestion of an increased likelihood of having asthma, this is

significant at the conventional 5% significance level.

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Model Checking In order to assess the Logistic regression models ability to represent the data we use a statistical test

called the Hosmer & Lemeshow test. It is based on grouping cases into 10 equally spaced groups of

risk and comparing the observed probability with the expected probability within each group. To

perform this in SPSS repeat the process as if you where performing the logistic regression, so

Analyse – Regression – Binary Logistic and click the box Options to get

Tick the box corresponding to the Hosmer-Lemeshow goodness of fit, then click Continue

followed by Ok. In the same output that you received before the following table should appear,

Hosmer and Lemeshow Test

Step Chi-square df Sig. 1 5.034 4 .284

In this case we have only included one variable (respdust) you may require there to be several

variables in your model. The above table may therefore have several steps, in that case then the last

step will always be the result for the final model. The model is deemed unsuitable if the p-value (in

the Sig column) is less than 0.05, therefore in this case as the p-value is 0.284 the model is deemed

to be adequate.

Exercise Examine the relationship between Bronchitis and dust levels using the methods above.

CREATING A SPSS SYNTAX To date we have used SPSS interactively, an alternative is to create an SPSS syntax file containing

the commands. There are two reasons for this: -

• It makes it easier and quicker to rerun an analysis if we make changes to the raw data.

• It documents the analysis that we have performed.

The screen shot below illustrates part of the syntax file for the analysis that we have done.

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This looks complicated but we do not need to learn this because SPSS can do this for us using the

menus. You may of notices a button paste on the interactive commands. We will illustrate this

using the t-test command. If we click paste instead of running the command then the syntax is

pasted into a new file.

The first time in a session that you click paste a new file is created. Using the same method as for

the t-test above you can add further commands to the syntax. It is possible to run the entire syntax

all at once or alternatively only specific commands.

To run the entire syntax click run on the options bar followed by all. To run a specific command,

highlight the command in the main window and click run followed by selection. A Syntax can be

edited through copy and paste commands or alternatively through more detailed written commands,

described in the help file. Because the syntax file is a separate file from SPSS needs to be saved

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separately at the end of the session, using File and Save. At the start of a new session, you can

reopen an existing syntax file.

Please find located along with the data set on the L:drive and/or the website address a syntax that

will give the appropriate output for the SPSS exercises within the notes.

By clicking File, Open and Syntax as shown above the following screen will appear.

Locate the appropriate file in

L: drive , spssco~1 which is named

foundrysyn.sps and click open.

The file can then be run in the same manner

described on the previous page.

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The syntax contains a set of codes and appropriate descriptions to produce the appropriate output

for each exercise (page 18 onwards) through out the notes. The descriptions of the tests performed

can be found in the syntax script after COMMENT.

Please note the first comment regarding the retrieval of the dataset.

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CHOOSING THE APPROPRIATE STATISTICAL PROCEDURE

In this tutorial we have illustrated some of the basic statistical procedures available in SPSS. These

are summarised in the table below.

Plausibly Continuous

and Normal

Ordinal or Ordered

Categorical

Binary and

Unordered

Categories

Comparison of

Independent Two

Groups

Box-plot

Independent groups t-

test

Box-plot or Cross-

tabulation of ordered

categories

Mann-Whitney U-test

Cross-tabulation

Chi-squared test

Fisher’s exact test

Comparison of more

than Two groups

Analysis of variance

(ANOVA)

Kruskal Wallis

analysis of Variance* Cross-tabulation

Chi-squared test

Comparison of two

related outcomes

Paired samples t-test Wilcoxon Matched

Pairs

McNemar’s Test

Relationship between

a dependent variable

and one or more

independent

variables

Scatter plot

Regression

Pearson's correlation

coefficient

Spearman correlation

or Kendall's

correlation coefficient

Phi coefficient

Logistic Regression

* Not illustrated

For a more comprehensive chart for selecting methods see

www.graphpad.com/www/book/choose.htm. We conclude by noting that SPSS has some serious

weaknesses for analysis of medical data. For example many of the methods give only p-values and

no confidence interval. For example the Mann-Whitney U-Test is a comparison of two medians but

it does not give the confidence interval for the difference of the two medians as recommended in

many guidelines for medical research publication. In this aspect the program StatsDirect, also

available in the Micro-labs, is much better as the corresponding procedure gives a confidence

interval of the difference between medians.